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Algorithmic trading firms were put on notice last Monday when
Commissioner Bart Chilton of the Commodity Futures Trading
Commission (CFTC) said that high frequency trading (HFT) and
algorithmic trading firms should be made legally responsible for
maintaining a minimum set of testing and monitoring standards to
prevent future flash crashes.

The possibility that algorithmic trading firms may need to
support new regulatory requirements highlights the rapidly
growing importance of the market data cloud and new cloud
services like NASDAQ Data-On-Demand for back testing purposes.

In a bid to resuscitate the heroes of 1980's sitcoms, Mr. Chilton
declared, "I want to be like MacGyver. Remember, he was always
trying to prevent crime before it happened." So if you're a
criminally culpable algorithmic trading firm, you'd best
beware-especially if Mr. Chilton gets his hands on a paper clip
and a stick of chewing gum. Household items aside, Mr. Chilton
also plans to rely on tools such as a "kind of Good Housekeeping
Seal of Approval." He'd like algorithmic trading systems to be
tested by either exchanges or regulators before going live. And
after going live, he'd like algorithmic trading systems to be
monitored on an on-going basis.

So, if you're engaged in algorithmic trading, you'll probably
want to make sure you've got systems in place to test your
algorithms so you're prepared for any regulatory requirements
that might come down the pike. That's where the market data cloud
comes in. If you want to future proof your algorithmic trading
systems against any possible testing requirements, you'll want to
look into how you can use the market data cloud to back test your
algorithms with minimal expense, time and effort put into
building new technology infrastructure.

On-demand market data cloud services like NASDAQ Data-On-Demand
offer a new way to hold down both the cost and time investments
of getting historical tick data to back test algorithmic trading
systems. You can get only the slice of data you need for your
specific test conditions, and you only pay for what you use, not
the entire universe of data, which dramatically reduces your data
costs. On-demand cloud services also let you get highly
customized data sets in a variety of common formats (XML, CSV,
JSON and so on) which reduces your need to build new technology
infrastructure just to load the data into your algorithmic
trading systems.

So if Mr. Chilton knocks at your door with the latest issue of
Good Housekeeping under one arm, never fear. Just check out
NASDAQ Data-On-Demand and rest assured you can handle any
algorithmic trading regulatory requirements for back testing that
he might have in mind without breaking the bank on new technology
infrastructure.